用大模型加速产品卖点生成，提升效率与精准度。
Accelerate Creation of Product Claims Using Generative AI
- 基于大模型上下文学习与微调，智能生成和优化卖点
- 通过虚拟消费者模拟，自动评估并排序卖点效果
- 已在快消品行业验证，适用于多品类跨行业应用
产品卖点是影响消费者购买决策的关键因素，但其创作耗时耗资。我们开发了名为《Claim Advisor》的网页应用，利用大语言模型（LLM）的上下文学习与微调技术，加速卖点的搜索、生成、优化与仿真。该系统具备三项功能：(1) 语义搜索并识别与消费者语境相符的现有卖点或视觉素材；(2) 根据产品描述与消费者画像生成或优化卖点；(3) 通过合成消费者模拟对生成或人工创建的卖点进行排序。在快消品公司中的应用已取得显著成效。我们认为该能力具有广泛适用性，可推广至多个产品类别与行业。我们分享经验，以推动生成式AI在各领域的研究与落地。
原文摘要 · Abstract (English)
The benefit claims of a product is a critical driver of consumers' purchase behavior. Creating product claims is an intense task that requires substantial time and funding. We have developed the $\textbf{Claim Advisor}$ web application to accelerate claim creations using in-context learning and fine-tuning of large language models (LLM). $\textbf{Claim Advisor}$ was designed to disrupt the speed and economics of claim search, generation, optimization, and simulation. It has three functions: (1) semantically searching and identifying existing claims and/or visuals that resonate with the voice of consumers; (2) generating and/or optimizing claims based on a product description and a consumer profile; and (3) ranking generated and/or manually created claims using simulations via synthetic consumers. Applications in a consumer packaged goods (CPG) company have shown very promising results. We believe that this capability is broadly useful and applicable across product categories and industries. We share our learning to encourage the research and application of generative AI in different industries.
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